… bi-fold system to detect and classify brain tumors into glioma, … performance of the single
classifiers and give weightage to … effective in dealing with tumor identification and classification…

… the problem of improving the efficiency and effectiveness of brain tumor classification. In the
… The feature selection methods help to improve the classifier algorithms for detecting benign …

… a classification model can be modeled effectively to … classifier that uses both the theory of
neuro-fuzzy system (NFS) and the adaptive boosting algorithm to the problem of classification. …

… , the proposed classification model can effectively reduce the complexity of the classification
… MRI brain tumor classification using Support Vector Machines and meta-heuristic method[C]…

… histological type, classification, grade, potential aggressiveness … the brain anatomy, imaging
characteristics of brain tumor as … , so it achieves an effective balance between sensitivity and …

… fusion method proposed in this paper is more effective than the recognition rate of each of …
with feature classifier is 79%, the recognition rate of GGCM combined with feature classifier is …

… In classification, the inner training points were used to replace the original … efficiency of
high-dimensional KNN algorithm classification. Johansson proposed an improved KNN classifier …

… Detection and classification of HGG and LGG brain tumor using machine learning[C]//32nd
International Conference on Information Networking(ICOIN),January 10-12,2018,Chiang Mai,…

… node efficiency, we found that the efficiency of some brain nodes … Support vector machine
(SVM) is used for classifier training, … the accuracy of depression classification reached 85.14%. …

… of tumor areas can be classified by linear kernel SVM classifier … Identification and classification
of brain tumor MRI images … An efficient and automatic glioblastoma brain tumor detection …